{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入算法包以及数据集\n",
    "from sklearn import neighbors\n",
    "from sklearn import datasets\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "x_data = iris.data[:,:2]\n",
    "y_data = iris.target\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = neighbors.KNeighborsClassifier()\n",
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(model):\n",
    "    # 获取数据值所在的范围\n",
    "    x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1\n",
    "    y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1\n",
    "\n",
    "    # 生成网格矩阵\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                         np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "    z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据\n",
    "    z = z.reshape(xx.shape)\n",
    "    # 等高线图\n",
    "    cs = plt.contourf(xx, yy, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+YmjVfVciNTlVVdUHXikiQ8BtInK+qj5cNCUyhlQ+ICLXAdcBdDnRR6x1Cgtr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\nKZbQ7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ecc2bee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7631578947368421"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输入数据建立模型\n",
    "knn.fit(x_train, y_train)\n",
    "plot(knn)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()\n",
    "# 准确率\n",
    "knn.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = tree.DecisionTreeClassifier()\n",
    "dtree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vqKpj21Uv89Xzbo1tz21Tl3LL7W9tqO3brnp56ULG0NiV+zbgKyLiEXXjfENV7xSRzwD7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ecf3150f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.6578947368421053"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输入数据建立模型\n",
    "dtree.fit(x_train, y_train)\n",
    "plot(dtree)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()\n",
    "# 准确率\n",
    "dtree.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kUpUtaZFwi92oFaERZ5DWy0mnCeIO34ja2jcuHpEFxTO7EnWumkAQT9HiLpVC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\n/wm0HsyjQg2vuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ecf34d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7894736842105263"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_knn = BaggingClassifier(knn, n_estimators=100)\n",
    "# 输入数据建立模型\n",
    "bagging_knn.fit(x_train, y_train)\n",
    "plot(bagging_knn)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()\n",
    "bagging_knn.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DmyivaBYW5/xpFo4/P87s4TxD2cZhqnEvy0h1KT7E9+4c9ALmXMhwwvGXbCWn2QRauXLf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\nV6KKNs7VqhZU8WXDr96ThjW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ecf4212b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7105263157894737"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_tree = BaggingClassifier(dtree, n_estimators=100)\n",
    "# 输入数据建立模型\n",
    "bagging_tree.fit(x_train, y_train)\n",
    "plot(bagging_tree)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()\n",
    "bagging_tree.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
